Statistical Remedies for Medical Researchers

ISBN-13: 9783030437138 / Angielski / Twarda / 2020 / 291 str.

Peter F. Thall

Recenzje

"This is an excellent book ... written primarily for medical researchers and statisticians working in the medical research sector. ... I'd conclude by extending my hearty congratulations to the author for contributing such a wonderful volume to the biostatistics literature." (Bibhas Chakraborty, International Statistical Review, Vol. 88 (3), 2020)

"This book can be recommended to a statistician that is starting a career in drug development and pharma research, or a statistician in this area willing to receive some food for thought and foster critical thinking." (ISCB News, iscb.info, Issue 70, December, 2020)

Spis treści

1 Why Bother With Statistics?.- 2 Frequentists and Bayesians.- 3 Knocking Down the Straw Man.- 4 Science and Belief.- 5 The Perils of P-values.- 6 Flipping Coins.- 7 All Mixed Up.- 8 Sex, Biomarkers, and Paradoxes.- 9 Crippling New Treatments.- 10 Just Plain Wrong.- 11 Getting Personal.- 12 Multistage Treatment Regimes.- References.

O autorze

Peter F. Thall is the Anise J. Sorrell Professor at the Department of Biostatistics at M.D. Anderson Cancer Center. He is a Fellow of the American Statistical Association and The Society for Clinical Trials, and received the Owen Award in 2014.  He has published over 250 papers and book chapters in the statistical and medical literature, and co-authored the book Bayesian Designs for Phase I-II Clinical Trials. His research areas include clinical trial design, precision medicine, Bayesian nonparametric statistics, incorporating expert opinion in Bayesian inference, and dynamic treatment regimes. He has presented over 200 invited talks and 30 short courses, and has served as an associate editor for Journal of the National Cancer Institute, Statistics in Medicine, Statistics in Biosciences, Clinical Trials, and Biometrics.

Blurb

This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious.  For each example, it provides one or more alternative statistical methods that avoid misleading or incorrect inferences being made. The technical level is kept to a minimum to make the book accessible to non-statisticians. At the same time, since many of the examples describe methods used routinely by medical statisticians with formal statistical training, the book appeals to a broad readership in the medical research community.